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Authors: Andreas L. Symeonidis 1 ; 2 ; 1 ; 2

Affiliations: 1 Aristotle University of Thessaloniki and 2Informatics and Telematics Institute, Greece ; 2 Aristotle University of Thessaloniki, Greece

ISBN: 978-989-8565-08-2

Keyword(s): Search Engine Optimization, LDArank, Semantic Analysis, Latent Dirichlet Allocation, LDA Gibbs Sampling, LDArank Java Application, Webpage Semantics, Semantic Analysis SEO.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Internet Technology ; Ontology and the Semantic Web ; Searching and Browsing ; Sensor Networks ; Signal Processing ; Soft Computing ; Web Information Systems and Technologies ; Web Interfaces and Applications ; Web Programming

Abstract: The added-value of search engines is, apparently, undoubted. Their rapid evolution over the last decade has transformed them into the most important source of information and knowledge. From the end user side, search engine success implies correct results in fast and accurate manner, while also ranking of search results on a given query has to be directly correlated to the user anticipated response. From the content providers’ side (i.e. websites), better ranking in a search engine result set implies numerous advantages like visibility, visitability, and profit. This is the main reason for the flourishing of Search Engine Optimization (SEO) techniques, which aim towards restructuring or enriching website content, so that optimal ranking of websites in relation to search engine results is feasible. SEO techniques are becoming more and more sophisticated. Given that internet marketing is extensively applied, prior quality factors prove insufficient, by themselves, to boost ranking and t he improvement of the quality of website content is also introduced. Current paper discusses such a SEO mechanism. Having identified that semantic analysis was not been widely applied in the field of SEO, a semantic approach is adopted, which employs Latent Dirichlet Allocation techniques coupled with Gibbs Sampling in order to analyze the results of search engines based on given keywords. Within the context of the paper, the developed SEO mechanism LDArank is presented, which evaluates query results through state-of-the-art SEO metrics, analyzes results’ content and extracts new, optimized content. (More)

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Paper citation in several formats:
Mavridis, T. and L. Symeonidis, A. (2012). IDENTIFYING WEBPAGE SEMANTICS FOR SEARCH ENGINE OPTIMIZATION.In Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8565-08-2, pages 272-275. DOI: 10.5220/0003937302720275

@conference{webist12,
author={Themistoklis Mavridis. and Andreas L. Symeonidis.},
title={IDENTIFYING WEBPAGE SEMANTICS FOR SEARCH ENGINE OPTIMIZATION},
booktitle={Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2012},
pages={272-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003937302720275},
isbn={978-989-8565-08-2},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - IDENTIFYING WEBPAGE SEMANTICS FOR SEARCH ENGINE OPTIMIZATION
SN - 978-989-8565-08-2
AU - Mavridis, T.
AU - L. Symeonidis, A.
PY - 2012
SP - 272
EP - 275
DO - 10.5220/0003937302720275

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